When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional neural networks, showing limitations in capturing long-range dependencies. In this paper, we formulate a simple yet principled One-stage Retinex-based Framework (ORF). ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. We design an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions. By plugging IGT into ORF, we obtain our algorithm, Retinexformer. Comprehensive quantitative and qualitative experiments demonstrate that our Retinexformer significantly outperforms state-of-the-art methods on thirteen benchmarks. The user study and application on low-light object detection also reveal the latent practical values of our method. Code, models, and results are available at https://github.com/caiyuanhao1998/Retinexformer

翻译：在低光照图像增强任务中，许多深度学习算法基于Retinex理论。然而，Retinex模型并未考虑暗区中隐藏的噪声或光照增强过程引入的失真。此外，这些方法通常需要繁琐的多阶段训练流程并依赖卷积神经网络，在捕捉长距离依赖关系方面存在局限性。本文提出了一种简洁且具有原理性的一阶段Retinex框架（ORF）。ORF首先估计光照信息以提升低光照图像的亮度，随后修复失真以生成增强后的图像。我们设计了一种光照引导Transformer（IGT），利用光照表示来指导不同光照条件下区域非局部交互的建模。通过将IGT嵌入ORF，我们得到了算法Retinexformer。全面的定量与定性实验表明，我们的Retinexformer在十三个基准测试中显著优于当前最先进方法。用户研究以及在低光照目标检测中的应用也揭示了该方法潜在的实际应用价值。代码、模型及结果见https://github.com/caiyuanhao1998/Retinexformer。